Chain Rule
A fundamental rule in calculus for computing the derivative of composite functions, and the mathematical basis of the backpropagation algorithm.
Timeline (last 90 days)
Sigmoid reparameterization maps unconstrained real-valued parameters to the (0,1) interval, naturally satisfying the boundary constraints after relaxation and enabling gradients to propagate back to the parameters through the chain rule.
Backpropagation's credit assignment solution is based on the chain rule, computing sensitivity layer by layer from the output end, and can obtain the gradient of each weight at a cost of roughly one forward pass.
The backpropagation algorithm is built entirely on the chain rule.
All Facts (3)
Sigmoid reparameterization maps unconstrained real-valued parameters to the (0,1) interval, naturally satisfying the boundary constraints after relaxation and enabling gradients to propagate back to the parameters through the chain rule.
50%UnverifiedBackpropagation's credit assignment solution is based on the chain rule, computing sensitivity layer by layer from the output end, and can obtain the gradient of each weight at a cost of roughly one forward pass.
50%UnverifiedThe backpropagation algorithm is built entirely on the chain rule.
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